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A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
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Mice have long served as models for studying human biology and pathology because of their phylogenetic and physiological similarity with humans. They are also easy to maintain and breed in the laboratory, and hence, many inbred strains are now available for research. Studies on mice have contributed immeasurably to our understanding of cancer biology.
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The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Introducción a los modelos fundacionales en patología

Brandon Veremis1, Shengjia Chen2, Gabriele Campanella2

  • 1Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, 1 Gustave L Levy Place, Box 1194, New York, NY, 10029, USA. brandon.veremis@mountsinai.org.

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Los modelos fundacionales, un tipo de aprendizaje automático, ofrecen información sobre patología sin necesidad de etiquetado experto. Estos modelos transformadores se están volviendo vitales para la investigación en patología y su posible uso clínico.

Palabras clave:
Visión por computadoraModelo fundacionalModelo de lenguaje grandeTransformadorTransformador de visión

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Área de la Ciencia:

  • Inteligencia artificial en medicina
  • Patología computacional
  • Aprendizaje automático para la atención médica

Sus antecedentes:

  • Los modelos fundacionales representan una nueva clase de algoritmos de aprendizaje automático.
  • Estos modelos aprovechan grandes conjuntos de datos y aprendizaje no supervisado, lo que reduce la necesidad de etiquetado de datos por expertos.
  • Se destacan en la generación de representaciones fundamentales de patrones de datos.

Objetivo del estudio:

  • Proporcionar una descripción general no técnica de los modelos transformadores para patólogos.
  • Explicar la utilidad de los modelos fundacionales en patología.
  • Destacar los modelos fundacionales de patología publicados y sus aplicaciones en investigación.

Principales métodos:

  • El estudio ofrece una descripción general conceptual de los modelos transformadores.
  • Analiza la aplicación de modelos fundacionales en flujos de trabajo de patología.
  • Los ejemplos ilustrativos utilizan datos de cáncer de cabeza y cuello dentro de un modelo fundacional disponible públicamente.

Principales resultados:

  • Los modelos fundacionales sirven como un paso inicial eficaz en proyectos de aprendizaje automático para patología.
  • Generan representaciones de datos internas aplicables a tareas posteriores como la clasificación de tumores y la predicción de biomarcadores.
  • El uso de modelos fundacionales puede optimizar la investigación en patología al reducir la dependencia del etiquetado manual de datos.

Conclusiones:

  • Los modelos fundacionales están preparados para aumentar su prevalencia en la investigación de aprendizaje automático en patología y en la práctica clínica.
  • Su capacidad para aprender de vastos conjuntos de datos y minimizar el etiquetado manual ofrece ventajas significativas.
  • Comprender estos modelos es crucial para los patólogos que participan en la IA en su campo.